Online-Journals.org (International Association of Online Engineering)
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Enhancing Mobile Learning with AI-Powered Chatbots: Investigating ChatGPT’s Impact on Student Engagement and Academic Performance
Get PDFIn mobile learning environments, after-class review strategies play a crucial role in reinforcing key concepts, summarizing knowledge, and enhancing subject mastery. However, students often encounter difficulties reviewing lessons due to limited support and immediate assistance, impacting their overall learning experience. This study examines the role of artificial intelligence (AI)-powered ChatGPT as a mobile learning tool to support pre-service students in academic performance, cognitive load reduction, perceived learning, trust, and motivation. Utilizing a quasi-experimental design, two classes enrolled in an assessment and evaluation course at UTM University, Malaysia, participated in the study. The experimental group (n = 16) engaged with ChatGPT via mobile devices for post-lesson reviews, while the control group (n = 16) relied on traditional instructor-led interactions. Pre- and post-tests and ANCOVA analyses were conducted to evaluate changes in students’ learning outcomes. The findings indicate that mobile AI-powered ChatGPT significantly enhances academic achievement, reduces cognitive load, and fosters increased motivation, perceived learning, and trust. The results highlight the potential of integrating AI-driven mobile learning solutions to provide personalized, on-demand academic support, enabling students to engage in more effective and flexible learning experiences beyond traditional classroom settings. These insights contribute to the growing body of research on AI in education, emphasizing the need for further exploration into mobile AI-driven interventions for diverse learning contexts
Construction of a Hybrid Learning Model Based on Mobile Interaction Technology in Vocational Schools and its Mechanism for the Quality Assurance of Teaching
Get PDFWith the continuous advancement of information technology and the widespread adoption of mobile internet and smart devices, profound transformations have been introduced into instructional models within vocational colleges. As an instructional approach that integrates both online and offline resources, the hybrid learning model has gradually emerged as a central direction for educational reform in vocational education. The application of mobile interaction technology has enabled students to engage in real-time and flexible learning interactions through digital platforms, thereby enhancing learning outcomes. However, insufficient attention has been paid to the identification and construction of implicit interaction relationships within hybrid learning environments—particularly on learning platforms underpinned by mobile interaction technology. The effective identification and optimization of implicit learner-to-learner interaction dynamics remain unresolved challenges. Current domestic and international research in hybrid learning and mobile learning platforms has primarily focused on evaluating instructional outcomes and integrating resources, while exploration into implicit interaction patterns has remained limited. Although certain studies have proposed strategies for optimizing hybrid learning models, these approaches often lack data-driven, systematic methodologies and fail to fully uncover implicit learner interactions and underlying needs. To address this study gap, this study seeks to explore methods for discovering and constructing implicit interaction relationships among students within mobile interaction-based learning platforms. Specifically, the study focuses on identifying implicit learner interactions in hybrid learning contexts and proposes a discovery method for such relationships based on an extended mobile interaction graph. The aim is to provide both theoretical grounding and practical guidance for vocational colleges to improve mechanisms that ensure instructional quality and enhance learning effectiveness
Research-Based Practice on the Implementation of a Brand Building Program in Higher Education: A Case Study
Get PDFThe context of the paper is, on the one hand, growing trend of portfolio-based competency development in higher education, especially focusing on assessing students’ progression, promoting self-evaluation, reflective thinking, career planning, self-directed and collaborative learning strategies [1], [7], [17], [19]. Under the umbrella of portfolio-based higher education, brand building programs play important role in this process [5]. On the other hand, brand building programs can develop self-management skills at individual and system levels as well. At individual level, brand building promotes students in higher education to develop competencies, which are based on the labor market needs in order to reduce the skill gap and promote reskilling. At the organizational level, the paper focusing on the process from hierarchy to professional community. The first purpose of the paper is to introduce some results of the implementation of a 4-year MyBrand Program, especially focusing on the supporting system of the implementation. MyBrand Program in Budapest Metropolitan University is a careerfocused education initiative that focuses on practice-oriented education and the skills needed in the workplace promoting the successful future careers of students and successful placement in the labor market in the world of work. MyBrand program is linked to the University’s new, “creative university” identity. This paper introduces some parts of the program implementation: MyBrand portfolio workshops and trainings, Teachers’ Day and Club, mentoring program, basic competence matrix, MyBrand teaching handbook, METU Learn curricula, Online Knowledge Hub. The support system being developed is based on qualitative research on implementation of MyBrand Program, namely content analysis and 14 in-depth interviews. The second purpose of the paper is to give practical examples and students’ feedback of career planning at the course of Innovative learning technology skills in becoming an engineer at Óbuda University. At the conclusion part, I summarize on the results and experience of implementation of brand building program and the experience of career planning, tutoring and self-management in practice
Bridging the Digital Divide: Leveraging Social Media for Enhanced Corporate Learning and Digital Literacy Among Older Adults
Get PDFThis paper addresses the innovative strategies adopted by the DigIN project in order to increase digital literacy among adults aged 55 years and above using social media platforms, particularly YouTube and Spotify. The project will create an active learning community by sharing relevant educational content tailored to meet the needs of older learners. Analytics gathered data from all platforms, showing key performance indicators, including views and engagement rates and insight into demographics. Preliminary findings indicate high engagement; for example, on the YouTube channel, there were 2120 views and a total of 42.9 hours of watching time, with 42% of viewers falling into the age group 55–64. What is more, those podcasts published on Spotify were highly rated, especially among Polish speakers, thus proving great interest in digital competencies locally. Results prove that social media can be quite a useful tool for corporate learning and human resources development, with the aim of singling out the need for tailored content, taking into consideration peculiar challenges that, in general, arise while teaching older adults. The implications, therefore, for future corporate training initiatives lie in the use of social media to create learning environments that empower underserved populations and, by extension, increase their independence and overall workforce competency
Development of an Advanced 4D Foot Shape Model from Multiple Acquisitions at Different Flexion Angles
Get PDFA major challenge in constructing models of the human body or body parts stems from the fact that they are actually composed of several solid bodies articulated together. Consequently, 3D models must be parameterised with reference to joint angles: scans of the foot taken at different plantar/dorsal flexion angles were used in this study as a benchmark. The implemented methodology was based on the regression fitting of landmark trajectories obtained on a limited number of foot poses; these same landmarks were then used to guide 3D mesh morphing to predict foot geometry at different plantar/dorsal flexion angles. A careful optimisation of the methodology was performed to identify the optimal set of foot scans. The comparison between the actual foot shapes obtained by 3D laser scanning and the predicted shapes showed that the average error is at most 6.57 ± 2.74 mm. The methodology indications were finally drawn based on these error estimates. Further works will consider combined motions including flexion/extension, internal/external rotation, and foot inversion/eversion
Predicting Markers of Cognitive Decline within Small Population Samples of Daily Life Activities
Get PDFLifestyle markers associated with health can be used to predict decline in cognitive function among individuals. The objective of this study was to investigate how lifestyle factors assessed from data surveys impact decline in cognitive function by employing a cognitive assessment questionnaire across subpopulations in three districts in India. Lifestyle attributes and their correlations to cognitive strength were identified using machine learning methods. Our analysis suggests that modifiable lifestyle factors, including physical activity, choice of smoking, social interaction, and following a regular diet, significantly impact changes in explicit and implicit memory, emphasizing the interconnectedness between lifestyle choices and cognitive function. Neuropsychological assessment scores for visuospatial and delayed recall memory abilities between male and female participants showed significant differences, highlighting the importance of considering sex differences in cognitive research and clinical practice. Lifestyle choices can have implications across perceived states of cognitive functions that can be crucial for public health and intelligent app development
The Impact of Robotic Technology in Vocational Education towards the Development of Industry 5.0: A Systematic Literature Review
Get PDFThe wrong use of learning models in the robotics learning process can cause students to not understand how to build a robotic system step by step. Likewise, the use of irrelevant technology in the learning process can result in low graduate competence, as the competencies possessed are not in accordance with industry needs. So, this study aims to assess the impact and how robotic technology is applied in vocational education. This study employs a systematic literature review methodology, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The review includes articles published between 2018 and 2024, focusing on the use of robotic technology in vocational education. A total of 26 relevant research studies were selected for analysis. The results of this study show that universities, vocational high schools, and robotics training institutions use robotics technology. The types of robots used include mobile robots, ARM robots, humanoid robots, animal robots, training kits, and robotics system simulator applications. Learning models integrated with this technology include project-based learning (PjBL), the most widely used, problem-based learning (PBL), computer-based robotics, hands-on learning, CPLM, pair learning, and EL-CP. Robotic technology impacts almost all knowledge domains, including cognitive, psychomotor, and affective
Utilizing Facial Emotion Analysis with FaceReader to Evaluate the Effects of Outdoor Activities on Preschoolers' Basic Emotions: A Pilot Study in e-Health
Get PDFThe theory of basic emotions posits that a set of fundamental emotions—happiness, sadness, anger, fear, disgust, and surprise—are associated with universally recognizable facial expressions. One method for studying facial expressions is the Facial Action Coding System (FACS), which allows researchers to measure facial muscles to identify the emotions expressed by participants. Preschool is an important period for the development of kids and their ability to understand emotions. In this stage, children’s physical activity (PA) levels tend to decrease significantly, with children spending more time sedentary, such as sitting for six to eight hours a day and engaging in screen-based activities. The present study aimed to assess the basic emotional states and neutral levels of preschoolers (aged 5–6 years) participating in outdoor activities, using FaceReader (FR) as a facial emotion analysis tool. The results indicate that the outdoor activity intervention had a significant impact on the emotional states of the experimental group (EG), with marked reductions in sadness (p = .002) and disgust (p = .01), as well as significant increases in valence (p = .01) and arousal (p = .004). These findings showed the importance of outdoor activities in reducing negative emotions among preschool-aged children
Illumination-Robust Conjunctival Image Preprocessing for Accurate Segmentation and Anemia Detection Using Deep Learning
Get PDFAnemia, defined by reduced hemoglobin or red blood cell levels, remains a critical public health issue, particularly in resource-limited settings where traditional diagnostics are inaccessible. Non-invasive detection via ocular conjunctiva imaging offers a viable solution but is challenged by illumination variability in outdoor environments. This study introduces a novel preprocessing pipeline to standardize conjunctival images, employing grayscale histogram normalization, LAB color space-based glare inpainting, and adaptive contrast enhancement to counter uneven lighting and reflections. Segmentation performance was assessed using U-Net, BiSeNet, and ConjunctiveNet; U-Net outperformed the others, achieving a precision of 84.22% with preprocessing versus 80.08% without preprocessing. For anemia classification, an artificial neural network (ANN), CNN-ResNet, and SLIC-GAT models were tested on the CP-AnemiC (Ghana) and Eyes-defy-anemia (India) datasets. Preprocessing significantly boosted ANN accuracy from 81.54% to 85.51% (Ghana) and 85.94% to 88.28% (India), with precision increasing by up to 6.33%. For CNN-ResNet, F1-scores improved from 81.91% to 89.15% (Ghana), while for ANN on the India dataset, F1-scores increased from 85.73% to 87.35%. These results highlight the pipeline’s ability to enhance segmentation accuracy and classification reliability, reducing false positives and enabling robust anemia detection under variable lighting, thus advancing non-invasive diagnostics for field applications
Social Learning and Gamification Strategies for Optimizing Online Learning in a Computer Science Course
Get PDFThis paper investigates the effectiveness of social and gamified learning strategies to assess their impact on computer science education, using a comparative analysis between the Moodle and Quizizz platforms. Gamified learning, with its interactive and competitive elements, is an ideal tool for fostering engagement and collaboration in social learning. The hypothesis of this study is that the integration of social and gamified elements can significantly improve student performance and motivation in computer science education. Group 1 took a traditional Moodle course without social interaction, Group 2 used Moodle with additional social interaction features, and Group 3 engaged with a gamified learning platform with integrated social interaction. Each group’s unique online learning environment aimed to provide nuanced insights with implications for educators, institutions and policymakers seeking to improve the effectiveness of computer science education in the digital landscape. The results of the study show a positive correlation between the implementation of gamification and increased student motivation, which ultimately translates into improved pass rates